If the accuracy of my current model is low ($50 \%$) and we want to minimize time in collecting more data, should we try other models?
To know if your model needs more training data, try to plot out "learning curves", that are based on increasing size of the training set.

Basically, you calculate training and validation accuracy metrics for 1, 2, 3, 4, 5, ..., m  training samples. Size of validation set may be constant over time. If the accuracy is still rising when your data set is fully used, then you need more training data.